Temporal Patterns of Fruit Fly (Drosophila) Evolution Revealed by Mutation Clocks (original) (raw)

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01 January 2004

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Koichiro Tamura, Sankar Subramanian, Sudhir Kumar, Temporal Patterns of Fruit Fly (Drosophila) Evolution Revealed by Mutation Clocks, Molecular Biology and Evolution, Volume 21, Issue 1, January 2004, Pages 36–44, https://doi.org/10.1093/molbev/msg236
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Abstract

Drosophila melanogaster has been a canonical model organism to study genetics, development, behavior, physiology, evolution, and population genetics for nearly a century. Despite this emphasis and the completion of its nuclear genome sequence, the timing of major speciation events leading to the origin of this fruit fly remain elusive because of the paucity of extensive fossil records and biogeographic data. Use of molecular clocks as an alternative has been fraught with non-clock-like accumulation of nucleotide and amino-acid substitutions. Here we present a novel methodology in which genomic mutation distances are used to overcome these limitations and to make use of all available gene sequence data for constructing a fruit fly molecular time scale. Our analysis of 2977 pairwise sequence comparisons from 176 nuclear genes reveals a long-term fruit fly mutation clock ticking at a rate of 11.1 mutations per kilobase pair per Myr. Genomic mutation clock–based timings of the landmark speciation events leading to the evolution of D. melanogaster show that it shared most recent common ancestry 5.4 MYA with D. simulans, 12.6 MYA with D. erecta+D. orena, 12.8 MYA with D. yakuba+D. teisseri, 35.6 MYA with the takahashii subgroup, 41.3 MYA with the montium subgroup, 44.2 MYA with the ananassae subgroup, 54.9 MYA with the obscura group, 62.2 MYA with the willistoni group, and 62.9 MYA with the subgenus Drosophila. These and other estimates are compatible with those known from limited biogeographic and fossil records. The inferred temporal pattern of fruit fly evolution shows correspondence with the cooling patterns of paleoclimate changes and habitat fragmentation in the Cenozoic.

Introduction

Inference of species divergence times in the fruit fly phylogeny that led to the evolution of Drosophila melanogaster has been hindered by the absence of extensive, reliable fossil records and biogeographic data. Molecular clocks are routinely used as an alternative tool to infer temporal speciation patterns in such cases (Easteal and Oakeshott 1985; Powell 1997; Hedges and Kumar 2003). In fruit fly evolutionary studies, however, it has been difficult to establish reliable molecular clock calibrations and to obtain unbiased time estimation because of the unequal substitution rates among species, even for synonymous substitutions (Eanes et al. 1996; Rodriguez-Trelles, Tarrio, and Ayala 1999; Tatarenkov et al. 1999; Rodriguez-Trelles, Tarrio, and Ayala 2000). Highly expressed genes show extreme preference for certain codons, which often leads to lower estimates of the number of synonymous substitution per site because of the effects of natural selection and estimation biases (Shields et al. 1988; Sharp and Li 1989; Dunn, Bielawski, and Yang 2001).

Furthermore, codon usage biases are known to vary significantly among lineages (Rodriguez-Trelles et al. 1999), which will lead to synonymous substitution rate variation among lineages. This poses severe problems while inferring molecular time scales of fruit fly evolution. For instance, the Hawaiian Drosophila species, for which the divergence time is considered to be the best point for calibrating molecular clocks, show much lower codon usage biases than D. melanogaster (Rodriguez-Trelles et al. 2000); the D. melanogaster Adh gene sequence shows a 36% higher codon adaptation index (CAI; Sharp and Li 1987) than the Hawaiian D. picticornis Adh gene sequence. This is also true even when we compute codon usage statistics independent of the knowledge of the optimal codons, such as the effective number of codons (Wright 1990); D. picticornis has a much higher effective number of codons (47.1) than D. melanogaster (36.1). This difference in codon usage biases is also reflected in the rejection of the homogeneity of the substitution patterns in the third codon positions at a 1% level when the disparity index test (Kumar and Gadagkar 2001) is used.

Because evolutionary divergences now used for building fruit fly molecular clocks directly employ the actual amount of synonymous or nonsynonymous change that has been permitted in sequence evolution, the intrinsic non-clock-like behavior of substitution accumulation has repeatedly impeded those efforts (Thomas and Hunt 1993; Russo, Takezaki, and Nei 1995; Rodriguez-Trelles, Tarrio, and Ayala 2001_a_, b). Therefore, to build molecular time scales, we need to estimate distances that are independent of the effects of codon usage bias and selection. Here we present a method for estimating mutation distance based on analysis of multiple genes (we refer to this as genomic mutation distance) and use it for inferring timing of major fruit fly speciation events.

Methods

Sequence Data Acquisition

cDNA and protein sequences for D. melanogaster were obtained from http://www.fruitfly.org/sequence/dlMfasta.shtml. All available sequences belonging to the genus Drosophila were downloaded from the National Center for Biotechnology Information (NCBI; GenBank). Redundant sequences were identified based on the annotated gene name and the sequence similarity, and were excluded. A BLASTP (Altschul et al. 1997) search was conducted using each D. melanogaster protein sequence as a query and the database of all other fruit fly sequences as the target set. For each set of BLAST-hits produced, we constructed an orthologous sequence data set by taking the best hit from each distinct species available. Sequences in each putative orthologous set were then checked for gene name to ensure the use of the same gene; GenBank protein identification numbers (protein id) were matched in case of gene name ambiguities. When multiple sequences were available for the same gene from a species, the longest sequence was chosen. The data for known genes from Bergman et al. (2002) were then added to this collection. Each protein sequence pair (obtained by translating the cDNA sequence) was aligned using Clustal W (Thompson, Higgins, and Gibson 1994), and the corresponding cDNA alignments were generated with the protein sequence alignments as guides. All analyses were conducted by using these pairs of aligned sequences.

Distance Estimation

Synonymous Distances

We used the number of nucleotide substitutions per fourfold-degenerate site as the measure of synonymous distance to avoid estimation biases from approximations needed to separate synonymous and nonsynonymous sites (Dunn, Bielawski, and Z. Yang 2001; Kumar and Subramanian 2002). A third codon position was considered fourfold degenerate only if it was fourfold degenerate in both sequences compared. To reduce estimation errors, only sequence pairs with 50 or more fourfold-degenerate sites were included. This produced 6,085 sequence pairs from 176 genes. Of these only 2,977 pairs involved in major divergence events that occurred in the lineage leading to D. melanogaster were used. The Tamura-Nei (Tamura and Nei 1993) method was used to correct for multiple hits in order to account for transition/transversion rate and base-composition biases. The Disparity index test (Kumar and Gadagkar 2001) revealed significant base composition differences among lineages as the stationarity of substitution pattern was rejected in 35% pairwise comparisons at a 5% level, confirming significant differences in codon usage among lineages. Therefore, we used the modified Tamura-Nei method (Tamura and Kumar 2002) to account for substitution pattern heterogeneity in fourfold-degenerate sites among lineages.

Genomic Mutation Distances

For computing genomic mutation distances, we express the relationship between synonymous distance (d Si) and the mutation distance (d_μ_i) for a given gene i by

where f i refers to the fraction of mutations underestimated due to selection against the unpreferred codons and estimation biases. This equation is parallel in concept to that well known for the neutral theory (Kimura 1983), showing that the rate of synonymous substitution becomes equal to the rate of point mutation under the strict neutrality. The synonymous distance for the genes with the smallest (or no) codon usage bias is one of the best candidates of the mutation distance, as there is the least selection or evolutionary distance estimation bias (e.g., Dunn, Bielawski, and Yang 2001). For instance, figure 1_a_ shows the well-known negative relationship between CAI and synonymous distance (Sharp and Li 1987; Shields et al. 1988) for the comparison of D. melanogaster and D. simulans, where the actual mutation distance is close to synonymous distances observed for genes with the smallest CAI. However, estimates of codon usage bias (e.g., CAI) have large variances (as they involve many parameters), and it is unclear what constitutes the minimum codon usage bias. Instead, we find that the base composition skew, BCS (Tamura and Kumar 2002), at synonymous sites, is directly related to CAI (fig. 1_b_). It shows a better linear relationship with synonymous distance than CAI (fig. 1_c_); _R_2 = 0.148 and 0.176 for CAI and BCS, respectively. BCS is also better because it can be computed with less sampling error (as it requires fewer parameters). Furthermore, in our preliminary analysis, BCS gave a smaller variance and higher correlation with d S than the G + C content of silent sites (_R_2 = 0.159), which is thought to be an alternative indirect measure of codon usage bias (Shields et al. 1988). These results also hold firm for species comparisons with much higher divergences: _melanogaster_-yakuba, _melanogaster_-pseudoobscura, and _melanogaster_-virilis (results not shown). Because genes with the lowest codon usage bias showed a BCS ≈ 0, the _y_-intercept in figure 1_c_ could be used as a bias-corrected estimate of the mutation distance.

Based on the observed linear relationship in figure 1_c_, we can express f i in equation (1) as a product of C i and η (f i = η_C_ i), where C i is the BCS for gene i and η is the fraction of mutations eliminated per unit BCS for the genome pair compared. We can now rewrite equation (1) to convert the observed synonymous distance (d_S_i) for a given gene into the estimate of number of mutations per site (d_μ_i) using the observed base composition skew for that gene (C i).

The value of η necessary for using equation (2) is estimated by using all genes for the given species pair. It is obtained by dividing the absolute value of the slope of the linear regression of BCS on d S by the _y_-intercept of the regression line. For the _melanogaster_-simulans pair, η = 3.3. We found that the estimates of η for other species pairs with many genes (e.g., _melanogaster_-yakuba, _melanogaster_-pseudoobscura, and _melanogaster_-virilis) were quite similar to that for _melanogaster_-simulans and statistically not significantly different at the 5% level (P = 0.74, 0.76, and 0.48 for _melanogaster_-yakuba, _melanogaster_-pseudoobscura, and _melanogaster_-virilis, respectively). Because the _melanogaster_-simulans comparison has the largest number of genes, η is estimated with the smallest error and is therefore used as a global value for estimating d_μ_i in all further analyses.

Results

Genomic Mutation Clocks

Relationship of Mutation Distance and Codon Adaptation

Figure 2_a_–d show that the estimated d_μ_i does not depend on the extent of codon adaptation in the analysis of closely as well as distantly related species. The multigene distributions of d_μ_i are symmetrical in nature and have a clear-cut central tendency (fig. 3); means of these distributions provide estimates of genomic mutation distances, _d_μ. This method for estimating genomic mutation distance is useful when building molecular time scales, as it allows for the combining of data from many different genes for different pairs of species because databases are gene rich for some species but poor for most others.

Relative Rate Tests and Mutation Clocks

With the ability to compute genomic mutation distances between species, we are in a position to estimate mutation rate and species divergence times as long as mutations accumulate in a clock-like fashion. Therefore, we conducted the relative rate tests (Beverley and Wilson 1984; Wu and Li 1985; Nei and Kumar 2000) of the mutational clock at different levels of evolutionary relatedness. Results from the relative rate tests for five pairs of species are shown in table 1. None of the tests show significant departure from the null hypothesis of mutation rate equality between lineages. Therefore, mutations appear to accumulate at similar rates among diverse Drosophila species, at least up to the _melanogaster_-pseudoobscura divergence.

Divergence Time Estimation

An accurate calibration point is required to anchor the molecular clock to estimate divergence times. This is provided by a well-established divergence time of 5.1 MYA between D. picticornis and other species belonging to the planitibia subgroup based on the time of formation of Kauai in Hawaii (Carson and Clauge 1995). This estimate is considered to be the most robust/reliable estimate and has been used in numerous studies (e.g., Rowan and Hunt 1991; Takezaki, Rzhetsky, and Nei 1995). With this estimate, the rate of point mutation was computed to be μ = 0.113/ (2 × 5.1 × 106) = 1.1 (±0.2) × 10−8 mutations per site per year per lineage, because the genomic mutation distance between D. picticornis and other species belonging to the planitibia subgroup was 0.113 ± 0.0218. We used this rate to convert the genomic mutation distance to time for all other species comparisons. The variance of divergence time (V t) was estimated by V t = _d_μ2/μ2(V d/_d_μ2 + _V_μ/μ2), where V d and _V_μ represent the variances of _d_μ and μ, respectively.

In the estimation of divergence times of the ananassae subgroup from the melanogaster subgroup, we found that the average of uncorrected synonymous distances was much higher than that for the _obscura_-melanogaster comparison. This is unexpected because the species in the ananassae subgroup are considered to be more closely related to D. melanogaster than those in the obscura group. In fact, in the ananassae-melanogaster sequence comparisons, the observed heterogeneity of substitution pattern was higher than that expected in >99% Monte-Carlo replicates of the disparity index test (Kumar and Gadagkar 2001) for 7 of 10 genes. For the other 3 genes, this percentage was >80%. This extreme substitution pattern heterogeneity in the ananassae lineage might be a reason for the high synonymous distance estimates. However, there were three genes that were shared by species belonging to the ananassae subgroup, melanogaster subgroup, and obscura group. In those genes, the mutation distance for the ananassae-melanogaster comparison was always lower than that for the _obscura_-melanogaster comparison. This allowed us to use those genes to estimate _ananassae_-melanogaster divergence time by using the obscura group is an outgroup. For each gene, we divided the mutation distance for the _ananassae_-melanogaster divergence by the mutation distance for the _obscura_-melanogaster divergence and multiplied it by the _obscura_-melanogaster divergence time to obtain the _ananassae_-melanogaster divergence time. Finally, the average divergence time was obtained from three gene-specific estimates to reduce the effect of gene sampling errors. This estimate will need to be refined in the future as more sequences become available.

Divergence Times in Fruit Fly Evolution

Figure 4 shows the species divergence times for taxon-pairs with multiple genes (3–64 genes); 2,977 pairwise sequence comparisons from 176 genes were used in this analysis. The estimated molecular time scales for major divergence events are in good accordance with the inferences from biogeography and fossil records. The divergence of melanogaster + simulans from orena + erecta and from yakuba + teissieri (12.6–12.8 MYA) is in agreement with previous inferences based on biogeography (Lachaise et al. 1988). A fossil of a Scaptomyza species was found in Dominican amber with a minimal age of 23 Myr (Grimaldi 1987). Because Scaptomyza originated in Hawaii from a common ancestor of the Hawaiian Drosophila (Tamura et al. 1995; Tatarenkov, Zurovcova, and Ayala 2001), their divergence time should be at least 23 Myr. A molecular estimate of 30.5 (± 6.6) MYA is consistent with this requirement. In a similar way, the oldest time estimate of 62.9 ± 12.4 MYA is consistent with the maximum possible divergence time of 80 Mya between subgenera Drosophila and Sophophora as inferred from biogeographic considerations (Beverley and Wilson 1984). These agreements support potential appropriateness of the calibration of the genomic mutation clock.

However, our genomic mutation clock-based estimates are significantly older for some speciation events that have been previously dated by molecular clock methods. In particular, the _melanogaster_-simulans divergence based on 62 genes is twice as old as thought before (Thomas and Hunt 1993; Russo, Takezaki, and Nei 1995); this result is statistically significant (P < 0.02; using a Z-test to compare our estimate with the currently accepted value of 2.5 MYA). Previous studies mainly used Adh gene data whose sequences are available from the largest number of Drosophila species. Although we have used the same calibration point (Hawaiian species divergence) as did the other studies, they did not account for codon usage bias differences between the Hawaiian species and other species. The effect of this factor is clearly evident in figure 5_a_ for the Adh gene data; the rate of uncorrected synonymous substitution for the Sophophora lineage leading to D. melanogaster is almost half of that for the subgenus Drosophila lineage leading to the Hawaiian species. This correlates with the difference in codon usage bias between D. melanogaster and Hawaiian D. picticornis, which produces much higher reduction in synonymous distances for the Sophophora lineage than those for the Hawaiian species and leads to considerable underestimation of the divergence times. After correcting for the effect of the codon usage bias difference on synonymous substitutions using equation (2), the mutation rate difference observed in Adh genes between the two subgenera disappears (fig. 5_b_). Consequently, our mutation distance for the Adh gene alone dated the _melanogaster_-simulans and melanogaster-yakuba divergence times to be 4.0 ± 1.7 and 13.7 ± 3.7 MYA, respectively, whereas the uncorrected synonymous distances for fourfold-degenerate sites suggested that they were 2.1 ± 0.9 and 6.8 ± 1.9 MYA. It is interesting to note that our older time estimates based on corrected synonymous distances are in a good agreement with those reported by Beverley and Wilson (1984). Therefore, it is important to use corrected synonymous distance (mutation distance) in building reliable fruit fly evolutionary timescales.

In figure 4, we have also presented three divergence times between very closely related species pairs, i.e., _pseudoobscura_-persimilis, _simulans_-mauritiana, and _pseudoobscura_-miranda. These estimates do not exclude the effect of ancestral population polymorphism, as population polymorphism data do not currently exist for many genes. Therefore, they may be considered to be maximum estimates and will need to be revised in the future.

Single Gene Molecular Clocks

The linear fit of species divergence times for the mutation distances observed for each individual gene, with divergence times estimated by using all the genes, is shown in table 2. For almost all genes shown, _R_2 values in linear regressions are very high for mutation distances and the average rates of mutation are rather similar. The multigene histogram of these mutation rates is shown in figure 6_a._ This distribution has the average rate (± 1 SE) of 0.0114 ± 0.0003. These mutation rates are contrasted with the nonsynonymous substitution rates for the same set of genes shown in figure 6_a._ The L-shaped distribution of the nonsynonymous substitution rates shows that Drosophila genes are under strong purifying selection, with only one gene showing a nonsynonymous substitution rate significantly higher than the average genomic mutation rate. This is the Acp26Aa gene, which is well known to be under strong positive selection (Tsaur and Wu 1997). Some of the other genes showing high nonsynonymous substitution rates were Acp29AB, mei-218, and rux; all of which have already been recognized as rapidly evolving at the protein sequence level (Aguade 1999; Avedisov et al. 2001; Manheim et al. 2002). However, the nonsynonymous rates were much lower than the average genomic mutation rate in all of these cases.

The protein distances for individual genes show much poorer linearity with species divergence times, as compared to that for mutation distances (table 2). Many genes for which amino acid substitutions are known not to follow a clock-like accumulation are clearly exposed. Both Adh and Gpdh show extremely poor fit with species divergence times (Rodriguez-Trelles et al. 2001_a_), suggesting that their protein evolution is not clock-like. In contrast, some genes such as amd, yellow, and rux appear to accumulate amino acid substitutions in a more clock-like fashion. It is notable that mutation and protein evolutionary rates do not show a significant correlation (fig. 6_b_), clearly establishing the independence of the estimated mutation and protein evolutionary rates.

Discussion

We have presented a method to correct the bias introduced by the codon adaptation when estimating evolutionary divergence at synonymous sites for inferring mutation distances. Our method works by employing the relationship of the codon usage bias with the synonymous divergence observed in the Drosophila genomes and produces a measure of the average genomic mutation distance. However, the codon usage bias is caused not only by selection but also by mutational bias in the strictly neutral sites. For example, the G+C contents of introns and noncoding regions are about 40% in a variety of Drosophila species, as the direction of mutation is biased toward nucleotides A and T (Moriyama and Hartl 1993; Bergman and Kreitman 2001). This suggests that the expected codon usage in the absence of codon selection is already biased and that BCS = 0 (used in our formulation) may not correspond to the case of zero codon adaptation. The violation of the assumption may lead to biased estimation of the mutation distance. However, it is well known from studies of mammalian genomes (e.g., Hughes and Yeager 1997) that the third codon position G+C content is about 10% higher than that observed in introns and intergenic regions. If this is true for Drosophila genomes as well, then we expect about 50% G+C content to be the baseline G+C content for the fourfold-degenerate sites in the Drosophila genomes. This is indeed the case because >97% genes show fourfold-degenerate site G+C content equal to or higher than 50% in our study. At any rate, it is virtually impossible to estimate the exact base composition at the strictly neutral synonymous sites. Therefore, the average genomic mutation rate reported here should be considered an approximation.

Comparative sequence analyses inherently generate average genomic rates among species. Therefore our estimates are average estimates among species. However, the similarity of base compositions of introns and noncoding regions over widely divergent Drosophila species (Moriyama and Hartl 1993; Bergman and Kreitman 2001) suggests that the mutation bias has not changed much during fruit fly evolution and that our estimates may be close approximations for species-specific mutation rates. (See, however, a small departure for D. willistoni reported by Bergman et al. [2002].) This permitted us to use genomic mutation distances from different gene sets from different species pairs to maximally utilize the available data in our analyses.

Relative rate tests conducted using the mutation distances at different levels of taxonomic divergence show that the null hypothesis of the presence of a mutation clock in diverse fruitfly species is not rejected. This allows for assuming a mutation clock and inferring a temporal pattern of species divergences during fruit fly evolution leading to D. melanogaster. In fact, the order of divergences for all the species groups (obscura and willistoni) and subgroups (takahashii, montium, and ananassae) belonging to the subgenus Sophophora from D. melanogaster (fig. 4) is consistent with previous studies for molecular phylogeny of Sophophora using multiple genes (Goto and Kimura 2001; O'Grady and Kidwell 2002).

On the basis of the divergence times estimated, it is interesting to speculate about the temporal pattern of speciation events in the evolutionary history leading to D. melanogaster. Figure 4 suggests that the speciation events have not occurred regularly in time, as several events are clustered. Three independent pairs of sibling species (_melanogaster_-simulans, _yakuba_-teissieri, and _orena_-erecta) diverged within a short time (5.4–6.8 MYA). Evolutionary divergences among these three pairs also occurred in a short span of time 10.4–12.8 MYA. The melanogaster subgroup (containing these six species) diverged from the cluster of the three major subgroups in the melanogaster group (containing ananassae, montium, and takahashii subgroups) during the Late to Middle Eocene (35–45 MYA). The Hawaiian Drosophila also diverged from the lineage leading to D. virilis during this time period. Finally, both the obscura and willistoni groups diverged from the melanogaster group about 55 to 62 MYA after the divergence of the subgenera Drosophila and Sophophora, close to the K-T boundary, 63 MYA. These clustered timings of divergence are compatible with “radiations” proposed by Throckmorton (1975) to explain coincidental distribution patterns of species from independent species groups.

Many of the clustered divergence times either coincide or fall close to the periods of major climate changes during the Cenozoic. Marine sediment records suggest that important cooling steps occurred during the Late Miocene (5.0–6.5 MYA) and the Middle Miocene (12–15 MYA) (Kennett 1995; Zachos et al. 2001) era that coincide with a number of divergence time estimates (fig. 4). By contrast, there is a paucity of speciation events during Oligocene to the Early Miocene, periods with relatively uniform climatic temperatures or warming. An event mapped to this period is the Hawaiian _Drosophila_-Scaptomyza split which occurred in Hawaii, where local volcanic activities are thought to be responsible for speciation (Carson 1992). There are also clear correspondences of older species divergence events with climatic cooling. Although we have many species from most of the major species groups and subgroups related to D. melanogaster in our analysis, speciation patterns for independent species groups and subgroups need to be examined with a number of genes to generalize these inferences. Nevertheless, if the observed correspondence between the time of species divergences and paleoclimate changes is true, it supports Wallace's hypothesis for a rapid species change resulting from climatic change (Wallace 1870_a_, b). In the present case, the factor is postulated to be climatic cooling in the Cenozoic. A major consequence of this cooling was an extensive increase in aridification in the middle to low latitude regions, which lead to expansions of savannas and grasslands as well as the fragmentation of forests (Kennett 1995) that were primary habitats of ancestral fruit fly species and populations (Throckmorton 1975). The adaptation to the newly arisen dry environment and the allopatry caused by the forest fragmentation are potential causes for stimulating fruit fly speciation. The former adaptation is supported by the distribution patterns of D. teissieri and D. yakuba, which are adapted to forests and savannas, respectively (Lachaise et al. 1988). The allopatric speciation is also plausible from the overlapping distribution patterns for independent species pairs, say, _D. melanogaster_-simulans and _D. teissieri_-yakuba (Lachaise et al. 1988).

Therefore the mutation clock proposed here provides opportunities to get an important glimpse of speciation processes and mechanisms when they are examined in the context of the contemporary earth history and environmental changes. Although the current view is still mostly speculative, the correlation between speciation events and Cenozoic climatic cooling will become better understood with accumulation of gene sequence data for other fruit fly species. The inferred temporal pattern of speciation from these efforts will be also useful in selecting genomes for sequencing and annotation, calibrating the tempo of DNA loss, building temporal contexts of origin and horizontal transfer events of the transposable elements, and understanding timing of gene duplications, chromosomal changes, and evolution of genome anatomies in general (Petrov and Hartl 1998; Silva and Kidwell 2000; Bergman et al. 2002; Kaminker et al. 2002).

Thomas Eickbush, Associate Editor

Fig. 1.

(a) Relationship of codon adaptation index (CAI) and the number of substitutions per fourfold-degenerate site (d S) between D. melanogaster and D. simulans (62 genes; _R_2 = 0.148). (b) Relationship between CAI and the base composition skew (BCS) in D. melanogaster genes (146 genes). (c) Relationships between BCS and d S between D. melanogaster and D. simulans (62 genes; _R_2 = 0.176). Fit of the linear regression line is shown in panels a and c. In panel c, higher order regression fit was statistically not significantly better than the linear regression fit shown

Fig. 2.

Relationship of estimated mutation distances (_d_μ) and codon adaptation index (CAI) for the comparison of D. melanogaster with (a) D. simulans, (b) D. yakuba, (c) D. psuedoobscura, and (d) D. virilis. The correlation between _d_μ and CAI is not significant in any of the cases (P > 0.7, 0.80, 0.3, and 0.4, respectively)

Fig. 3.

Multigene mutation distance histogram for comparisons between D. melanogaster and D. simulans (mel-sim), yakuba (mel-yak), pseudoobscura (mel-pse), and virilis (mel-vir). For direct comparison, each multigene distribution was standardized by using the average genomic distance for the corresponding species pair

Fig. 4.

A Drosophila evolutionary time scale based on genomic mutation distances. The number of genes (in parentheses) and standard errors are shown along with representative taxa. Paleoclimatic cooling periods are shown in light blue on the left of the geologic time scale (Kennett 1995; Zachos et al. 2001). sg. subgenus; gr. species group; sgr. species subgroup

Fig. 5.

The relationship of synonymous distances (a) and mutation distances (b) with time for the Adh gene in the subgenus Sophophora lineage leading to D. melanogaster (filled circle) and in the subgenus Drosophila leading to the Hawaiian D. picticornis (open circle). Regression analysis shows slopes of 0.011 and 0.005 for subgenus Sophophora and Drosophila, respectively, in panel a. These values are 0.012 and 0.010 in panel b.

Fig. 6.

(a) Histograms showing distributions of mutation rates and nonsynonymous substitution rates (nucleotide substitution rate at nondegenerate sites) in 176 genes. The arrow points to the nonsynonymous substitution rate of the Acp26Aa gene, which is higher than the average rate of the mutation distances. See text for details. (b) Relationship between genomic mutation and protein sequence evolution rates based on Poisson corrected distances. All rates are in the units of per site per billion years per lineage

Table 1

Relative Rate Tests for the Mutation Clock in Drosophila.

Note.—Relative rate tests are conducted for all the cases where multiple genes are shared by species 1, 2, and the outgroup. d_μ was computed using all genes available for species 1 and 2 following equation (2). The difference between lineage lengths (Δ_l) from the ancestor to species 1 and 2 is statistically not significant (ns) at a 5% significance level, so these relative rate tests do not reject the null hypothesis of mutation rate constancy. Z is the value of the normal deviate for the observed lineage length difference (Δ_l_) from 0.

Table 1

Relative Rate Tests for the Mutation Clock in Drosophila.

Note.—Relative rate tests are conducted for all the cases where multiple genes are shared by species 1, 2, and the outgroup. d_μ was computed using all genes available for species 1 and 2 following equation (2). The difference between lineage lengths (Δ_l) from the ancestor to species 1 and 2 is statistically not significant (ns) at a 5% significance level, so these relative rate tests do not reject the null hypothesis of mutation rate constancy. Z is the value of the normal deviate for the observed lineage length difference (Δ_l_) from 0.

Table 2

Genomic Mutation and Amino Acid Substitution Rates per Site per Billion Years per Lineage for Fruit Fly Genes.

Note.—Rates and linear regression fits (_R_2) are shown for genes where at least five well-spaced time points corresponding to distinct speciation events were available. All linear regression analyses were conducted with the intercept forced through the origin. Adh: alcohol dehydrogenase; amd: alpha methyl dopa-hypersensitive; Cid: centromeric histone; Gpdh: glycerol-3-phosphate dehydrogenase; l'sc: lethal of scute; rux: roughex.

Table 2

Genomic Mutation and Amino Acid Substitution Rates per Site per Billion Years per Lineage for Fruit Fly Genes.

Note.—Rates and linear regression fits (_R_2) are shown for genes where at least five well-spaced time points corresponding to distinct speciation events were available. All linear regression analyses were conducted with the intercept forced through the origin. Adh: alcohol dehydrogenase; amd: alpha methyl dopa-hypersensitive; Cid: centromeric histone; Gpdh: glycerol-3-phosphate dehydrogenase; l'sc: lethal of scute; rux: roughex.

We thank Adriana Briscoe, Thomas Dowling, Michael Rosenberg, Martin Wojciechowski, Margaret Kidwell, and Alan Filipski for their comments on an earlier draft of this manuscript. We also thank Balaji Ramanujum for providing invaluable technical assistance. This work was supported by a research grant from the Ministry of Education, Culture, Sports, Science and Technology, Japan, to K.T., and from the National Science Foundation, National Institutes of Health, and Burroughs Wellcome Fund, USA, to S.K.

Literature Cited

Aguade, M.

1999

. Positive selection drives the evolution of the Acp29AB accessory gland protein in Drosophila.

Genetics

152

:

543

-551.

Altschul, S. F., T. L. Madden, A. A. Schaffer, J. Zhang, and Z. Zhang, et al.

1997

. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

Nucleic Acids Res.

25

:

3389

-3402.

Avedisov, S. N., I. B. Rogozin, E. V. Koonin, and B. J. Thomas.

2001

. Rapid evolution of a cyclin A inhibitor gene, roughex, in Drosophila.

Mol. Biol. Evol.

18

:

2110

-2118.

Bergman, C. M., and M. Kreitman.

2001

. Analysis of conserved noncoding DNA in Drosophila reveals similar constraints in intergenic and intronic sequences.

Genome Res.

11

:

1335

-1345.

Bergman, C. M., B. D. Pfeiffer, D. E. Rincon-Limas, R. A. Hoskins, and A. Gnirke, et al.

2002

. Assessing the impact of comparative genomic sequence data on the functional annotation of the Drosophila genome. Genome Biol 3:RESEARCH0086–0086.

Beverley, S. M., and A. C. Wilson.

1984

. Molecular evolution in Drosophila and the higher Diptera II. A time scale for fly evolution.

J. Mol. Evol.

21

:

1

-13.

Carson, H. L.

1992

. Inversions in Hawaiian Drosophila,. Pp. 407–439 in C. B. Krimbas and J. R. Powell, eds. Drosophila inversion polymorphism. CRC Press, Boca Raton, Fla.

Carson, H. L., and D. A. Clauge.

1995

. Geology and biogeography of Hawaii,. Pp. 14–29 in W. L. Wagner and V. A. Funk, eds. Hawaiian biogeography: evolution on a hot spot archipelago. Smithsonian Institution Press, Washington, D.C.

Dunn, K. A., J. P. Bielawski, and Z. Yang.

2001

. Substitution rates in Drosophila nuclear genes: implications for translational selection.

Genetics

157

:

295

-305.

Eanes, W. F., M. Kirchner, J. Yoon, C. H. Biermann, and I. N. Wang, et al.

1996

. Historical selection, amino acid polymorphism and lineage-specific divergence at the G6pd locus in Drosophila melanogaster and D. simulans.

Genetics

144

:

1027

-1041.

Easteal, S., and J. G. Oakeshott.

1985

. Estimating divergence times of Drosophila species from DNA-sequence comparisons.

Mol. Biol. Evol.

2

:

87

-91.

Goto, S. G., and M. T. Kimura.

2001

. Phylogenetic utility of mitochondrial COI and nuclear Gpdh genes in Drosophila.

Mol. Phylogenet. Evol.

18

:

404

-422.

Grimaldi, D. A.

1987

. Amber fossil Drosophilidae (Diptera), with particular reference to the Hispaniolan taxa.

Am. Mus. Novitates

2880

:

1

-23.

Hedges, S. B., and S. Kumar.

2003

. Genomic clocks and evolutionary timescales.

Trends Genet.

19

:

200

-206.

Hughes, A. L., and M. Yeager.

1997

. Comparative evolutionary rates of introns and exons in murine rodents.

J. Mol. Evol.

45

:

125

-130.

Kaminker, J. S., C. M. Bergman, B. Kronmiller, J. Carlson, and R. Svirskas, et al.

2002

. The transposable elements of the Drosophila melanogaster euchromatin: a genomics perspective. Genome Biol. 3:RESEARCH0084–0084.

Kennett, J. P.

1995

. A Review of polar climatic evolution during the Neogene, based on the marine sediment record,. Pp. 49–64 in E. S. Vrba, G. H. Denton, T. C. Partridge and L. H. Burckle, eds. Paleoclimate and evolution, with emphasis on human origins. Yale University Press, New Heaven, Conn.

Kimura, M.

1983

. The neutral theory of molecular evolution. Cambridge University Press, Cambridge, U.K.

Kumar, S., and S. R. Gadagkar.

2001

. Disparity index: a simple statistic to measure and test the homogeneity of substitution patterns between molecular sequences.

Genetics

158

:

1321

-1327.

Kumar, S., and S. Subramanian.

2002

. Mutation rates in mammalian genomes.

Proc. Natl. Acad. Sci. USA

99

:

803

-808.

Lachaise, D., M. L. Cariou, J. R. David, F. Lemeunier, and L. Tsacas, et al.

1988

. Historical biogeography of the Drosophila-Melanogaster species subgroup.

Evol. Biol.

22

:

159

-225.

Manheim, E. A., J. K. Jang, D. Dominic, and K. S. McKim.

2002

. Cytoplasmic localization and evolutionary conservation of MEI-218, a protein required for meiotic crossing-over in Drosophila.

Mol. Biol. Cell

13

:

84

-95.

Moriyama, E. N., and D. L. Hartl.

1993

. Codon usage bias and base composition of nuclear genes in Drosophila.

Genetics

134

:

847

-858.

Nei, M., and S. Kumar.

2000

. Molecular evolution and phylogenetics. Oxford University Press, Oxford, New York.

O'Grady, P. M., and M. G. Kidwell.

2002

. Phylogeny of the subgenus sophophora (Diptera: Drosophilidae) based on combined analysis of nuclear and mitochondrial sequences.

Mol. Phylogenet. Evol.

22

:

442

-453.

Petrov, D. A., and D. L. Hartl.

1998

. High rate of DNA loss in the Drosophila melanogaster and Drosophila virilis species groups.

Mol. Biol. Evol.

15

:

293

-302.

Powell, J. R.

1997

. Progress and prospects in evolutionary biology: the Drosophila model. Oxford University Press, New York.

Rodriguez-Trelles, F., R. Tarrio, and F. J. Ayala.

1999

. Switch in codon bias and increased rates of amino acid substitution in the Drosophila saltans species group.

Genetics

153

:

339

-350.

2000

. Fluctuating mutation bias and the evolution of base composition in Drosophila.

J. Mol. Evol.

50

:

1

-10.

2001

. Erratic overdispersion of three molecular clocks: GPDH, SOD, and XDH.

Proc. Natl. Acad. Sci. USA

98

:

11405

-11410.

2001

. Xanthine dehydrogenase (XDH): episodic evolution of a “neutral” protein.

J. Mol. Evol.

53

:

485

-495.

Rowan, R. G., and J. A. Hunt.

1991

. Rates of DNA change and phylogeny from the DNA sequences of the alcohol dehydrogenase gene for five closely related species of Hawaiian Drosophila.

Mol. Biol. Evol.

8

:

49

-70.

Russo, C. A. M., N. Takezaki, and M. Nei.

1995

. Molecular phylogeny and divergence times of drosophilid species.

Mol. Biol. Evol.

12

:

391

-404.

Sharp, P. M., and W. H. Li.

1987

. The Codon Adaptation Index—a measure of directional synonymous codon usage bias, and its potential applications.

Nucleic Acids Res.

15

:

1281

-1295.

1989

. On the rate of DNA sequence evolution in Drosophila.

J. Mol. Evol.

28

:

398

-402.

Shields, D. C., P. M. Sharp, D. G. Higgins, and F. Wright.

1988

. Silent sites in Drosophila genes are not neutral—evidence of selection among synonymous codons.

Mol. Biol. Evol.

5

:

704

-716.

Silva, J. C., and M. G. Kidwell.

2000

. Horizontal transfer and selection in the evolution of P elements.

Mol. Biol. Evol.

17

:

1542

-1557.

Takezaki, N., A. Rzhetsky, and M. Nei.

1995

. Phylogenetic test of the molecular clock and linearized trees.

Mol. Biol. Evol.

12

:

823

-833.

Tamura, K., and S. Kumar.

2002

. Evolutionary distance estimation under heterogeneous substitution pattern among lineages.

Mol. Biol. Evol.

19

:

1727

-1736.

Tamura, K., and M. Nei.

1993

. Estimation of the number of nucleotide substitutions in the control region of mitochondrial-DNA in humans and chimpanzees.

Mol. Biol. Evol.

10

:

512

-526.

Tamura, K., G. Toba, J. Park, and T. Aotsuka.

1995

. Origin of Hawaiian drosophilids inferred from alcohol dehydrogenase gene sequences,. Pp. 9–18 in M. Nei and N. Takahata, eds. Current topics on molecular evolution: proceedings of the US-Japan workshop. The Pennsylvania State University, USA, Graduate School for Advanced Studies, Hayama, Japan.

Tatarenkov, A., J. Kwiatowski, D. Skarecky, E. Barrio, and F. J. Ayala.

1999

. On the evolution of Dopa decarboxylase (Ddc) and Drosophila systematics.

J. Mol. Evol.

48

:

445

-462.

Tatarenkov, A., M. Zurovcova, and F. J. Ayala.

2001

. Ddc and amd sequences resolve phylogenetic relationships of Drosophila.

Mol. Phylogenet. Evol.

20

:

321

-325.

Thomas, R. H., and J. A. Hunt.

1993

. Phylogenetic relationships in Drosophila: a conflict between molecular and morphological data.

Mol. Biol. Evol.

10

:

362

-374.

Thompson, J. D., D. G. Higgins, and T. J. Gibson.

1994

. Clustal-W-improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.

Nucleic Acids Res.

22

:

4673

-4680.

Throckmorton, L. H.

1975

. The phylogeny, ecology, and geography of Drosophila. Pp. 421–469 in R. C. King, ed. Handbook of genetics. Plenum Press, New York.

Tsaur, S. C., and C. I. Wu.

1997

. Positive selection and the molecular evolution of a gene of male reproduction, Acp26Aa of Drosophila.

Mol. Biol. Evol.

14

:

544

-549.

Wallace, A. R.

1870

. The measurement of geological time I.

Nature

1

:

399

-401.

Wallace, A. R.

1870

. The measurement of geological time II.

Nature

1

:

425

-455.

Wright, F.

1990

. The “effective number of codons” used in a gene.

Gene

87

:

23

-29.

Wu, C. I., and W. H. Li.

1985

. Evidence for higher rates of nucleotide substitution in rodents than in man.

Proc. Natl. Acad. Sci. USA

82

:

1741

-1745.

Zachos, J., M. Pagani, L. Sloan, E. Thomas, and K. Billups.

2001

. Trends, rhythms, and aberrations in global climate 65 Ma to present.

Science

292

:

686

-693.

Society for Molecular Biology and Evolution

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